




'''simple docstring'''


# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class      A       (						_a	,_a	,_a	,unittest.TestCase       ):
							lowercase_							     =							StableDiffusionControlNetImgaImgPipeline
							lowercase_							     =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
							lowercase_							     =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowercase_							     =							IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}       )
							lowercase_							     =							IMAGE_TO_IMAGE_IMAGE_PARAMS







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Union[str, Any]:


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							UNetaDConditionModel(
														    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,     cross_attention_dim=32				,     )
														torch.manual_seed(0   )
														_a      	=							ControlNetModel(
														    block_out_channels=(32, 64)				,     layers_per_block=2				,     in_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     cross_attention_dim=32				,     conditioning_embedding_out_channels=(16, 32)				,     )
														torch.manual_seed(0   )
														_a      	=							DDIMScheduler(
														    beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule='''scaled_linear'''				,     clip_sample=lowerCAmelCase_				,     set_alpha_to_one=lowerCAmelCase_				,     )
														torch.manual_seed(0   )
														_a      	=							AutoencoderKL(
														    block_out_channels=[32, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,     latent_channels=4				,     )
														torch.manual_seed(0   )
														_a      	=							CLIPTextConfig(
														    bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     )
														_a      	=							CLIPTextModel(lowerCAmelCase_   )
														_a      	=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

														_a      	=							{
														    '''unet''': unet,
														    '''controlnet''': controlnet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[Any]=0   )     ->				List[Any]:


														"""simple docstring"""


														if str(lowerCAmelCase_   ).startswith('''mps'''   ):
																					_a      	=							torch.manual_seed(lowerCAmelCase_   )
														else:
																					_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )

														_a      	=							2
														_a      	=							randn_tensor(
														    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,     generator=lowerCAmelCase_				,     device=torch.device(lowerCAmelCase_   )				,     )
														_a      	=							floats_tensor(control_image.shape				,     rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
														_a      	=							image.cpu().permute(0				,     2				,     3				,     1   )[0]
														_a      	=							Image.fromarray(np.uinta(lowerCAmelCase_   )   ).convert('''RGB'''   ).resize((64, 64)   )
														_a      	=							{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''output_type''': '''numpy''',
														    '''image''': image,
														    '''control_image''': control_image,
														}

														return inputs







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[Any]:


														"""simple docstring"""


														return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3   )







							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()				,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,     )
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[Any]:


														"""simple docstring"""


														self._test_inference_batch_single_identical(expected_max_diff=2e-3   )



class      A       (						_a	,_a	,unittest.TestCase       ):
							lowercase_							     =							StableDiffusionControlNetImgaImgPipeline
							lowercase_							     =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
							lowercase_							     =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowercase_							     =							frozenset([]       )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							UNetaDConditionModel(
														    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,     cross_attention_dim=32				,     )
														torch.manual_seed(0   )

														def init_weights(lowerCAmelCase_			:    List[str]   ):
																					if isinstance(lowerCAmelCase_				,     torch.nn.Convad   ):
																												torch.nn.init.normal(m.weight   )
																												m.bias.data.fill_(1.0   )

														_a      	=							ControlNetModel(
														    block_out_channels=(32, 64)				,     layers_per_block=2				,     in_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     cross_attention_dim=32				,     conditioning_embedding_out_channels=(16, 32)				,     )
														controlneta.controlnet_down_blocks.apply(lowerCAmelCase_   )

														torch.manual_seed(0   )
														_a      	=							ControlNetModel(
														    block_out_channels=(32, 64)				,     layers_per_block=2				,     in_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     cross_attention_dim=32				,     conditioning_embedding_out_channels=(16, 32)				,     )
														controlneta.controlnet_down_blocks.apply(lowerCAmelCase_   )

														torch.manual_seed(0   )
														_a      	=							DDIMScheduler(
														    beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule='''scaled_linear'''				,     clip_sample=lowerCAmelCase_				,     set_alpha_to_one=lowerCAmelCase_				,     )
														torch.manual_seed(0   )
														_a      	=							AutoencoderKL(
														    block_out_channels=[32, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,     latent_channels=4				,     )
														torch.manual_seed(0   )
														_a      	=							CLIPTextConfig(
														    bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     )
														_a      	=							CLIPTextModel(lowerCAmelCase_   )
														_a      	=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

														_a      	=							MultiControlNetModel([controlneta, controlneta]   )

														_a      	=							{
														    '''unet''': unet,
														    '''controlnet''': controlnet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]=0   )     ->				Tuple:


														"""simple docstring"""


														if str(lowerCAmelCase_   ).startswith('''mps'''   ):
																					_a      	=							torch.manual_seed(lowerCAmelCase_   )
														else:
																					_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )

														_a      	=							2

														_a      	=							[
														    randn_tensor(
														        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,     generator=lowerCAmelCase_				,     device=torch.device(lowerCAmelCase_   )				,     ),
														    randn_tensor(
														        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,     generator=lowerCAmelCase_				,     device=torch.device(lowerCAmelCase_   )				,     ),
														]

														_a      	=							floats_tensor(control_image[0].shape				,     rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
														_a      	=							image.cpu().permute(0				,     2				,     3				,     1   )[0]
														_a      	=							Image.fromarray(np.uinta(lowerCAmelCase_   )   ).convert('''RGB'''   ).resize((64, 64)   )
														_a      	=							{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''output_type''': '''numpy''',
														    '''image''': image,
														    '''control_image''': control_image,
														}

														return inputs







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.get_dummy_components()
														_a      	=							self.pipeline_class(**lowerCAmelCase_   )
														pipe.to(lowerCAmelCase_   )

														_a      	=							1_0.0
														_a      	=							4

														_a      	=							self.get_dummy_inputs(lowerCAmelCase_   )
														_a      	=							steps
														_a      	=							scale
														_a      	=							pipe(**lowerCAmelCase_   )[0]

														_a      	=							self.get_dummy_inputs(lowerCAmelCase_   )
														_a      	=							steps
														_a      	=							scale
														_a      	=							pipe(**lowerCAmelCase_				,     control_guidance_start=0.1				,     control_guidance_end=0.2   )[0]

														_a      	=							self.get_dummy_inputs(lowerCAmelCase_   )
														_a      	=							steps
														_a      	=							scale
														_a      	=							pipe(**lowerCAmelCase_				,     control_guidance_start=[0.1, 0.3]				,     control_guidance_end=[0.2, 0.7]   )[0]

														_a      	=							self.get_dummy_inputs(lowerCAmelCase_   )
														_a      	=							steps
														_a      	=							scale
														_a      	=							pipe(**lowerCAmelCase_				,     control_guidance_start=0.4				,     control_guidance_end=[0.5, 0.8]   )[0]

														# make sure that all outputs are different
														assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3
														assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3
														assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3   )







							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()				,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,     )
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Tuple:


														"""simple docstring"""


														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Dict:


														"""simple docstring"""


														self._test_inference_batch_single_identical(expected_max_diff=2e-3   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.get_dummy_components()
														_a      	=							self.pipeline_class(**lowerCAmelCase_   )
														pipe.to(lowerCAmelCase_   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )
														with tempfile.TemporaryDirectory() as tmpdir:
																					try:
																												# save_pretrained is not implemented for Multi-ControlNet
																												pipe.save_pretrained(lowerCAmelCase_   )
																					except NotImplementedError:
																												pass



@slow
@require_torch_gpu
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Optional[int]:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''   )

														_a      	=							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5'''				,     safety_checker=lowerCAmelCase_				,     controlnet=lowerCAmelCase_   )
														pipe.enable_model_cpu_offload()
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							torch.Generator(device='''cpu'''   ).manual_seed(0   )
														_a      	=							'''evil space-punk bird'''
														_a      	=							load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''   ).resize((5_12, 5_12)   )
														_a      	=							load_image(
														    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''   ).resize((5_12, 5_12)   )

														_a      	=							pipe(
														    lowerCAmelCase_				,     lowerCAmelCase_				,     control_image=lowerCAmelCase_				,     generator=lowerCAmelCase_				,     output_type='''np'''				,     num_inference_steps=50				,     strength=0.6				,     )

														_a      	=							output.images[0]

														assert image.shape == (5_12, 5_12, 3)

														_a      	=							load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''   )

														assert np.abs(expected_image - image   ).max() < 9e-2








"""simple docstring"""





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "allenai/led-base-16384": 16_384,
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = LEDTokenizer
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']





      def __init__(			self ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase="replace" ,		lowercase="<s>" ,		lowercase="</s>" ,		lowercase="</s>" ,		lowercase="<s>" ,		lowercase="<unk>" ,		lowercase="<pad>" ,		lowercase="<mask>" ,		lowercase=False ,		lowercase=True ,		**lowercase ,		)						->	Any:
            super().__init__(
                lowercase ,		lowercase ,		tokenizer_file=lowercase ,		errors=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		sep_token=lowercase ,		cls_token=lowercase ,		unk_token=lowercase ,		pad_token=lowercase ,		mask_token=lowercase ,		add_prefix_space=lowercase ,		trim_offsets=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
            if pre_tok_state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                  lowerCAmelCase    		=			getattr(lowercase ,		pre_tok_state.pop("""type"""					)					)
                  lowerCAmelCase    		=			add_prefix_space
                  lowerCAmelCase    		=			pre_tok_class(**lowercase					)

            lowerCAmelCase    		=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowerCAmelCase    		=			"""post_processor"""
            lowerCAmelCase    		=			getattr(self.backend_tokenizer ,		lowercase ,		lowercase					)
            if tokenizer_component_instance:
                  lowerCAmelCase    		=			json.loads(tokenizer_component_instance.__getstate__()					)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowerCAmelCase    		=			tuple(state["""sep"""]					)
                  if "cls" in state:
                        lowerCAmelCase    		=			tuple(state["""cls"""]					)

                  lowerCAmelCase    		=			False

                  if state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                        lowerCAmelCase    		=			add_prefix_space
                        lowerCAmelCase    		=			True

                  if state.get("""trim_offsets""" ,		lowercase					) != trim_offsets:
                        lowerCAmelCase    		=			trim_offsets
                        lowerCAmelCase    		=			True

                  if changes_to_apply:
                        lowerCAmelCase    		=			getattr(lowercase ,		state.pop("""type"""					)					)
                        lowerCAmelCase    		=			component_class(**lowercase					)
                        setattr(self.backend_tokenizer ,		lowercase ,		lowercase					)





      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def        _snake_case     (			self					)						->	str:
            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""					)
                  return None
            return str(self._mask_token					)





      @mask_token.setter
      def        _snake_case     (			self ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase ,		rstrip=lowercase					) if isinstance(lowercase ,		lowercase					) else value
            lowerCAmelCase    		=			value





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._batch_encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            lowerCAmelCase    		=			self._tokenizer.model.save(lowercase ,		name=lowercase					)
            return tuple(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase=None					)						->	Any:
            lowerCAmelCase    		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = PaddingStrategy.DO_NOT_PAD ,		lowercase = None ,		lowercase = None ,		)						->	dict:
            lowerCAmelCase    		=			super()._pad(
                encoded_inputs=lowercase ,		max_length=lowercase ,		padding_strategy=lowercase ,		pad_to_multiple_of=lowercase ,		return_attention_mask=lowercase ,		)

            # Load from model defaults
            if return_attention_mask is None:
                  lowerCAmelCase    		=			"""attention_mask""" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  lowerCAmelCase    		=			encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  lowerCAmelCase    		=			len(encoded_inputs["""global_attention_mask"""]					) != len(lowercase					)

                  if needs_to_be_padded:
                        lowerCAmelCase    		=			len(lowercase					) - len(encoded_inputs["""global_attention_mask"""]					)

                        if self.padding_side == "right":
                              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                              lowerCAmelCase    		=			(
                                  encoded_inputs["""global_attention_mask"""] + [-1] * difference
                              )
                        elif self.padding_side == "left":
                              lowerCAmelCase    		=			[-1] * difference + encoded_inputs[
                                  """global_attention_mask"""
                              ]
                        else:
                              raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

            return encoded_inputs






